Neonatal necrotizing enterocolitis(NEC)is a serious intestinal disease that often occurs in preterm and low birth weight infants,and delayed treatment may lead to serious complications such as neurodevelopmental delay,poor growth and development,gastrointestinal stricture,short bowel syndrome,intestinal failure,early diagnosis and intervention can help improve its prognosis.Preterm birth,enteral feeding,abnormal colonization of intestinal flora,intestinal mucosal ischemia,and infection are the main risk factors for the occurrence of NEC,but the specific pathogenesis is not fully understood.Clinical prediction models(CPMs)is a probability model that uses mathematical formulas to estimate the current individual may have a disease or a future outcome.In recent years,CPMs constructed by clinical features,laboratory indicators,biological markers,microorganisms and other information plays an important role in the diagnosis of NEC.However,the results of the study are inconsistent,and there is a lack of summary generalizations of the NEC diagnostic predictive model to advance clinical practice.This paper reviews the NEC diagnostic models constructed by traditional statistical methods and machine learning(ML).
维普
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